{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# {meth}`~tvm.arith.analyzer.Analyzer.rewrite_simplify`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/pc/data/lxw/ai/tvm-book/doc/read/arith\n"
     ]
    }
   ],
   "source": [
    "%cd ..\n",
    "import testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import inspect\n",
    "\n",
    "import pytest\n",
    "\n",
    "import tvm\n",
    "import tvm.testing\n",
    "from tvm import te, tir\n",
    "from tvm.tir import floordiv as fld\n",
    "from tvm.tir import floormod as flm\n",
    "from tvm.tir import truncdiv as tdiv\n",
    "from tvm.tir import truncmod as tmod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## {meth}`~tvm.arith.analyzer.Analyzer.rewrite_simplify` Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z = te.var(\"x\"), te.var(\"y\"), te.var(\"z\")\n",
    "x64 = te.var(\"x\", dtype=\"int64\")\n",
    "vx = te.var(\"vx\", dtype=\"int32x2\")\n",
    "vc = te.var(\"vc\", dtype=\"uint1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
